We propose an algorithm to detect wormhole attacks based on the Address Resolution Protocol (ARP) for mobile networks which use On-demand Distance Vector (AODV) routing protocol. This novel approach differs from all existing methods, requiring no extra hardware or software equipment, and offers a simple detection method by avoiding complex calculations. Wormhole attacks, being among the most harmful, can result in data theft, discarding, tampering, and can even facilitate other attacks. Current defense algorithms typically require additional hardware or software and involve complex calculations at each node, sometimes failing in certain scenarios. Our algorithm aims to overcome these limitations.
The application of reflective intelligent surfaces (RISs) combined with the vast bandwidth available in the THz spectrum band is an enabling technology that can substantially enhance the performance of industrial networks in the sixth generation of wireless communications (6G). In this context, this work analyzes the performance improvement achieved by deploying multiple RISs in a system operating at 140 GHz in an indoor factory (InF) scenario. The analysis is based on ray tracing simulations using a geometric model representing an InF environment. The effects of specular reflection, diffraction, diffuse scattering, and atmospheric molecular absorption are considered in the propagation modeling. The performance of the networks is measured based on the achievable data rate (ADR), and the gain from using RISs is quantified by comparing it to the performance of a system without the RIS aid. According to the simulation results, the application of five RISs with 1000 reflecting elements promotes an increase of 4.1 bit/s/Hz in the average achievable rate when compared to the non-RIS-assisted system.
Single-pixel imaging is a promising technique for Terahertz (THz) imaging, because it can largely reduce the numbers of expensive THz emitter and detector. Single-pixel imaging uses compressive sensing (CS) to measure the compressed THz signal and to recover the object image. This work presents a low-complexity and high-quality total variation augmented Lagrangian method (2D-TVALM) algorithm based on the well-known total variation augmented Lagrangian alternating-direction algorithm (TVAL3). TVAL3 is a convex optimization algorithm used to solve the problem of total variation (TV) regularization. By designing the sampling matrix and some mathematic approximation, this work largely reduces the computational complexity and speed up the convergence. The simulation results show that the reconstruction quality of the proposed algorithm is about 7 dB higher than TVAL3 in PSNR and the performance is much better than that of the other CS algorithms. In the computation time, the proposed algorithm is about 31 times faster than TVAL3. Finally, the average PSNR and elapsed time of the proposed algorithm are 38.43 dB and 0.0558 s respectively with 200 iterations in the 15 test patterns.
Filter banks play a crucial role in optimizing signal allocation and quality in wireless communication systems, addressing challenges like channelization and interference. However, traditional filter bank schemes suffer from high computational complexity and hardware costs. This paper proposes a novel approach for the hardware implementation of multidimensional filter banks, which significantly reduces computational complexity while maintaining high throughput. The underlying idea is to employ the in-memory computing (IMC) technique to perform filtering computations in a fully parallel manner using memristive devices. Additionally, this scheme offers scalability and flexibility, allowing it to realize multiple filters with diverse impulse responses and filter sizes.
In non-cooperative contexts, a receiver must estimate the transmitter’s communication parameters to recover information. Particularly in multi-user direct sequence spread spectrum systems, it is crucial to estimate the number of users and determine the length and pattern of each spreading sequence. Various studies estimating the spreading sequence have assumed that the number of users is known beforehand. And, the methods employed by these studies in an attempt to justify this assumption have inherent issues, such as peak ambiguity and threshold setting. To address these issues, this paper proposes a deep learning-based blind estimation algorithm for determining the number of users using the autocorrelation fluctuation extracted from the input signal. Through computer simulations, we show that the proposed method achieves high estimation accuracy even at low signal-to-noise ratio.
